Application of mathematics on crime detention
Table Of Contents
Chapter ONE
INTRODUCTION
- 1.1Introduction
- 1.2Background of Study
- 1.3Problem Statement
- 1.4Objective of Study
- 1.5Limitation of Study
- 1.6Scope of Study
- 1.7Significance of Study
- 1.8Structure of the Research
- 1.9Definition of Terms
Chapter TWO
LITERATURE REVIEW
- 2.1Overview of Literature Review
- 2.2Historical Perspectives
- 2.3Theoretical Framework
- 2.4Empirical Studies
- 2.5Methodologies Used in Previous Studies
- 2.6Key Findings from Literature
- 2.7Current Trends and Gaps
- 2.8Relevance to Current Study
- 2.9Critique of Existing Literature
- 2.10Summary of Literature Review
Chapter THREE
RESEARCH METHODOLOGY
- 3.1Research Design
- 3.2Sampling Techniques
- 3.3Data Collection Methods
- 3.4Data Analysis Procedures
- 3.5Research Instrumentation
- 3.6Ethical Considerations
- 3.7Reliability and Validity
- 3.8Limitations of Methodology
Chapter FOUR
DATA PRESENTATION AND ANALYSIS
- Discussion of Findings
- 4.1Overview of Findings
- 4.2Analysis of Data
- 4.3Comparison with Objectives
- 4.4Interpretation of Results
- 4.5Relationship to Literature
- 4.6Implications of Findings
- 4.7Recommendations for Future Research
- 4.8Practical Applications
Chapter FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
- and Summary
- 5.1Summary of Findings
- 5.2Conclusion
- 5.3Contribution to Knowledge
- 5.4Implications for Practice
- 5.5Recommendations
- 5.6Areas for Future Research
Project Abstract
The application of mathematics in the field of crime detention has gained significant attention in recent years due to its potential to enhance decision-making processes and improve the efficiency of law enforcement agencies. This research explores the various ways in which mathematical modeling and analysis can be utilized to address key challenges in crime detection and prevention. One of the primary areas where mathematics plays a crucial role in crime detention is predictive policing. By analyzing historical crime data and identifying patterns and trends, mathematical models can be developed to predict when and where crimes are likely to occur. This proactive approach enables law enforcement agencies to allocate resources more effectively and deploy officers to high-risk areas, ultimately reducing crime rates and enhancing public safety. Mathematics also plays a vital role in criminal network analysis, where complex relationships between individuals involved in criminal activities are studied using graph theory and network analysis techniques. By representing criminal networks as graphs and applying algorithms to identify key players and communication patterns, law enforcement agencies can dismantle criminal organizations more efficiently and disrupt illegal activities. Furthermore, mathematical optimization techniques are used to improve resource allocation and decision-making processes in crime detention. By formulating the problem of resource allocation as a mathematical optimization model, law enforcement agencies can determine the optimal distribution of resources such as patrol cars, officers, and surveillance equipment to maximize the impact on crime reduction while operating within budget constraints. Moreover, mathematical algorithms are employed in forensic analysis to process and interpret evidence gathered from crime scenes. Techniques such as pattern recognition, machine learning, and data mining are used to analyze fingerprints, DNA samples, and other forensic evidence, aiding investigators in identifying suspects and solving crimes more quickly. In conclusion, the application of mathematics in crime detention offers immense potential to revolutionize the way law enforcement agencies approach crime detection and prevention. By leveraging mathematical modeling, analysis, and optimization techniques, law enforcement agencies can make more informed decisions, allocate resources more effectively, and enhance their overall effectiveness in combating crime. This research underscores the importance of integrating mathematics into the field of crime detention to improve public safety and reduce criminal activities.
Project Overview
<p>
</p><p><strong>Introduction</strong></p><p>Mathematics is mental activity which consists in carrying out, one after the other, and those mental constructions which are inductive and effective. Meaning that by combining fundamental ideas, one reaches a definite result.</p><p>The importance of math in the administration of justice has risen with the growth of identification forensics and its influence continues to permeate questions of proof and judgment. For example, statistics (evidence) and</p><p>Mathematics is fast becoming one of the most important techniques in crime detection. Where once a Sherlock Holmes would have had to be content with a magnifying glass, or a jury with gut instinct and rational discussion, now a range of methods from probability and statistics are available to help. Today, mathematics lies behind expert conclusions on a hundred forensic matters from fingerprints to DNA.</p><p>1.1 Background of the Study</p><p>The application of mathematics to crime detection has proved rather successful in many ramifications.</p><p>Another area which can involve rather subtle mathematics is DNA identification. For detection purposes, thirteen particular pairs of genes are identified, amongst the many thousand that make up our DNA, and these thirteen pairs are so varied from person to person that the estimated chance of two people (not identical twins) having the same thirteen is just one in 400 trillion, far greater than the population of the world. Thus, when forensic biologists have a good quality sample to work with, they can make an unchallenged identification. But they often have to work with crime scene samples that are very tiny, mixed, or degraded. In these cases, identification can be made to a given individual only with a certain probability, and it is essential to be able to interpret this probability correctly.</p><p>A man was recently tried in San Francisco for a 30-year-old rape and murder, on the grounds that a DNA match was found between a semen sample stored in the cold-case files and an entry in a database of California sex offenders. Furthermore, the crime sample was degraded, so that it would actually match about one person in a million, roughly 300 people in the general population. There was virtually no other evidence against the defendant. The defense held that with a chance in a million of a match in the general population, running the sample through a database containing about one-third of a million individuals led to a chance of 1 in 3 of finding a random match to an innocent person. As for the prosecution, they cited the one in a million figure, which runs the risk of being misinterpreted as the defendant’s chance of being innocent (the “prosecutor’s fallacy”). The trouble is that both conclusions are wrong. The defense argument ignores two essential facts: firstly, that the 300 matching individuals are evenly distributed in age and geography around the country, not concentrated in a database of California sex offenders, and secondly the non-negligible probability that the original murderer may actually have been in the database for other offenses. For the prosecution, when using the one in a million figure, they must specify that the DNA alone only narrows the pool of potential murderers down to about 300 individuals, and must then use the facts that the unique database match turned out to be to a man who shared several characteristics with the original murderer, namely age, race (according to an eyewitness statement), location, and being a sex offender (whether registered or not), to narrow the field. Using these factors, the probability of the defendant’s innocence can be assessed as being less than about one in seventy. The fate of defendants can hinge on such calculations being made rigorously. It is essential to examine the errors that are most</p><p>Frequently made, learn to avoid them, and to establish controlled mathematical procedures that will be valid in a court of law.</p><p><strong>Coralie Colmez (2013)</strong>.The research seek to investigate the application of mathematics in crime detection</p><p>1.2 Statement of the Problem</p><p>Crime detection and investigation used to depend mostly on witnesses, hearsay or forced confessions. The first modern crime detection organization was Scotland Yard, established in the 19th century. Crime detection begins with the discovery of a crime scene, and proceeds through the process of evidence collection, identification and analysis. Crime scene investigation employs many forensic techniques, examining hairs or fibers, firearms, anatomy, bodily fluids and chemistry. Crime detection and investigation used to depend mostly on witnesses, hearsay or forced confessions. The first modern crime detection organization was Scotland Yard, established in the 19th century. Crime detection begins with the discovery of a crime scene, and proceeds through the process of evidence collection, identification and analysis Crime scene investigation employs many forensic techniques, examining hairs or fibers, firearms, anatomy, bodily fluids and chemistry. Surveillance is used when there is a high probability of a crime taking place at a specific place and time. Detectives are bound by all privacy laws, and must obtain a court order to intrude on privacy. Interrogation is probably the oldest crime detection and investigation technique. Detectives interview all known victims or witnesses and interrogate suspects to further their investigation. However the application of mathematics to crime detection seems not to have been fully utilized. Hence the problem confronting this research is to determine the application of mathematics on crime detection. </p><p>1.3 Objective of the Study</p><p>1 To determine the nature of mathematics</p><p>2 To determine the application of mathematics in the detection of crime.</p><p>1.4 Research Questions</p><p>1 What is the nature of mathematics?</p><p>2 What is the nature of the application of mathematics in the detection of crime?</p><p>1.5 Significance of the Study</p><p>The study provides a framework of reference for the application of mathematics in the detection of crime.</p><p>1.6 Statement of Hypothesis</p><p>1 Ho The level of crime is low</p><p> Hi The level of crime is high</p><p>2 Ho Mathematics is not significant</p><p> Hi Mathematics is significant</p><p>3 Ho The application of mathematics in crime detection is low</p><p> Hi The application of mathematics in crime detection is high</p><p>1.7 Scope of the Study</p><p>The study focuses on the application of mathematics in the detection of crime</p><p>1.8 Definition of Terms</p><p><strong>CRIME SCIENCE DEFINED</strong></p><p>The study of crime in order to find ways to prevent it. Three features distinguish crime science from criminology: it is single-minded about cutting crime, rather than studying it for its own sake; accordingly it focuses on crime rather than criminals; and it is multidisciplinary, notably recruiting scientific methodology rather than relying on social theory.</p><p>MATHEMATICS DEFINED</p><p>Mathematics is mental activity which consists in carrying out, one after the other, and those mental constructions which are inductive and effective. Meaning that by combining fundamental ideas, one reaches a definite result.</p><p>CRIME DETECTION</p><p>Crime detection begins with the discovery of a crime scene, and proceeds through the process of evidence collection, identification and analysis.</p><p>CRIME INVESTIGATION</p><p> Crime scene investigation employs many forensic techniques, examining hairs or fibers, firearms, anatomy, bodily fluids and chemistry</p>
<br><p></p>